Preprints
https://doi.org/10.5194/bg-2021-195
https://doi.org/10.5194/bg-2021-195

  24 Aug 2021

24 Aug 2021

Review status: this preprint is currently under review for the journal BG.

Importance of the forest state in estimating biomass losses from tropical forests: combining dynamic forest models and remote sensing

Ulrike Hiltner1,2,3, Andreas Huth1,4,5, and Rico Fischer1 Ulrike Hiltner et al.
  • 1Department of Ecological Modelling, Helmholtz-Centre for Environmental Research GmbH - UFZ, 04318 Leipzig, Germany
  • 2Institute of Geography, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, 91058 Germany
  • 3Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland
  • 4German Centre for Integrative Biodiversity Research – iDiv Halle-Jena-Leipzig, 04103 Leipzig, Germany
  • 5Institute for Environmental Systems Research, University Osnabruck, 49076 Osnabruck, Germany

Abstract. Disturbances, such as extreme weather events, fires, floods, and biotic agents, can have strong impacts on the dynamics and structures of tropical forests. In the future, the intensity of disturbances will likely further increase, which may have more serious consequences for tropical forests than those we have already observed. Thus, quantifying aboveground biomass loss of forest stands due to tree mortality (hereafter biomass loss) is important for the estimation of the role of tropical forests in the global carbon cycle. So far, the long-term impacts of altered tree mortality on rates of biomass loss have been described little.

This study aims to analyse the consequences of long-term elevated tree mortality rates on forest dynamics and biomass loss. We applied an individual-based forest model and investigated the impacts of permanently increased tree mortality rates on the growth dynamics of humid, terra firme forests in French Guiana. Here, we focused on biomass, leaf area index (LAI), forest height, productivity, forest age, quadratic mean stem diameter, and biomass loss. Based on the simulations, we developed a multiple linear regression model to estimate biomass losses of forests in different successional states from the various forest attributes.

The findings of our simulation study indicated that increased tree mortality altered the succession patterns of forests in favour of fast-growing species, which changed the forests’ gross primary production, though net primary production remained stable. Tree mortality intensity had a strong influence on the functional species composition and tree size distribution, which led to lower values in LAI, biomass, and forest height at the ecosystem level. We observed a strong influence of a change in tree mortality on biomass loss. Assuming a doubling of tree mortality, biomass loss increased (from 3.2 % y−1 to 4.5 % y−1). We also obtained a multidimensional relationship that allowed for the estimation of biomass loss from forest height and LAI. Via an example, we applied this relationship to remote sensing data of LAI and forest height and mapped biomass loss for French Guiana. We estimated a mean biomass loss rate of 3.2 % per year.

The approach described here provides a novel methodology for quantifying biomass loss, taking the successional state of tropical forests into account. Quantifying biomass loss rates may help to reduce uncertainties in the analysis of the global carbon cycle.

Ulrike Hiltner et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on bg-2021-195', Anonymous Referee #1, 14 Sep 2021
  • RC2: 'Review of bg-2021-195', Thomas Pugh, 12 Oct 2021

Ulrike Hiltner et al.

Ulrike Hiltner et al.

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Short summary
Quantifying biomass losses due to tree mortality is important for estimating the role of tropical forests in the global carbon cycle. We aim to analyse the consequences of long-term elevated tree mortality on tropical forest dynamics and biomass loss. Based on model simulations, we developed a statstical model to estimate biomass losses of forests in different successional states from forest attributes. Assuming a doubling of tree mortality, biomass loss increased from 3.2 % y−1 to 4.5 % y−1.
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